Jeong Hoon Lee, Cynthia Xinran Li, Hassan Jahanandish, Indrani Bhattacharya, Sulaiman Vesal, Lichun Zhang, Shengtian Sang, Moon Hyung Choi, Simon John Christoph Soerensen, Steve Ran Zhou, Elijah Richard Sommer, Richard Fan, Pejman Ghanouni, Yuze Song, Tyler M. Seibert, Geoffrey A. Sonn, Mirabela Rusu
Accurate prostate cancer diagnosis remains challenging. Even when using MRI, radiologists exhibit low specificity and significant inter-observer variability, leading to potential delays or inaccuracies in identifying clinically significant cancers. This leads to numerous unnecessary biopsies and risks of missing clinically significant cancers. Here we present prostate vision contrastive network (ProViCNet), prostate organ-specific vision foundation models for Magnetic Resonance Imaging (MRI) and Trans-Rectal Ultrasound imaging (TRUS) for comprehensive cancer detection. ProViCNet was trained and validated using 4,401 patients across six institutions, as a prostate cancer detection model on radiology images relying on patch-level contrastive learning guided by biopsy confirmed radiologist annotations. ProViCNet demonstrated consistent performance across multiple internal and external validation cohorts with area under the receiver operating curve values ranging from 0.875 to 0.966, significantly outperforming radiologists in the reader study (0.907 versus 0.805, p<0.001) for mpMRI, while achieving 0.670 to 0.740 for TRUS. We also integrated ProViCNet with standard PSA to develop a virtual screening test, and we showed that we can maintain the high sensitivity for detecting clinically significant cancers while more than doubling specificity from 15% to 38% (p<0.001), thereby substantially reducing unnecessary biopsies. These findings highlight that ProViCNet's potential for enhancing prostate cancer diagnosis accuracy and reduce unnecessary biopsies, thereby optimizing diagnostic pathways.
AI generated annotations for Breast, Brain, Liver, Lungs and Prostate cancer collections in National Cancer Institute Imaging Data Commons
BAMF Health（BAMF健康公司）；National Institute of Health（美国国立卫生研究院）；Frederick National Laboratory for Cancer Research（弗雷德里克国家癌症研究实验室）；Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）
HistoEncoder: a digital pathology foundation model for prostate cancer
University of Helsinki（赫尔辛基大学）；Helsinki University Hospital（赫尔辛基大学医院）；Skåne University Hospital（斯科讷大学医院）；Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki（赫尔辛基大学HiLIFE项目下属芬兰分子医学研究所（FIMM））；iCAN Digital Precision Cancer Medicine Flagship（iCAN数字精准癌症医学旗舰项目）
Assessing the Performance of Deep Learning for Automated Gleason Grading in Prostate Cancer
University of Augsburg（奥格斯堡大学）；University Hospital Augsburg（奥格斯堡大学医院）；Neu-Ulm University of Applied Sciences（新乌尔姆应用科学大学）；Bavarian Cancer Research Center（巴伐利亚癌症研究中心）；BioM Biotech Cluster Development GmbH（BioM生物技术集群发展有限公司）
Cross-Modal Self-Attention Distillation for Prostate Cancer Segmentation
University of Shanghai for Science and Technology（上海理工大学）；Tongji University（同济大学）；National University of Singapore（新加坡国立大学）；Shanghai Fire Corps Hospital（上海消防总队医院）；Shanghai Tenth People’s Hospital（上海市第十人民医院）
Location-based Radiology Report-Guided Semi-supervised Learning for Prostate Cancer Detection
National Cancer Institute（美国国家癌症研究所）；National Institutes of Health（美国国立卫生研究院）；Clinical Center, National Institutes of Health（美国国立卫生研究院临床中心）；Center for Cancer Research, National Cancer Institute（美国国家癌症研究所癌症研究中心）
Segmentation Strategies in Deep Learning for Prostate Cancer Diagnosis: A Comparative Study of Mamba, SAM, and YOLO
School of Electrical Engineering, Iran University of Science and Technology（伊朗科学技术大学电气工程学院）；School of Computer Engineering, Iran University of Science and Technology（伊朗科学技术大学计算机工程学院）；School of Electrical Engineering, Shahrood Branch, Islamic Azad University（伊斯兰阿扎德大学沙赫鲁德分校电气工程学院）；Medical Sciences and Technologies Department, Science and Research Branch, Islamic Azad University（伊斯兰阿扎德大学科学研究分支医学与技术系）；Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology（伊朗科学技术大学电气工程学院生物医学工程系）
PROFUSEme: PROstate Cancer Biochemical Recurrence Prediction via FUSEd Multi-modal Embeddings
Division of Computational Pathology, Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA（计算病理学部，病理与实验室医学部，印第安纳大学医学院，印第安纳波利斯，印第安纳州，美国）；Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, IN, USA（印第安纳大学Melvin和Bren Simon综合癌症中心，印第安纳波利斯，印第安纳州，美国）；Luddy School of Informatics, Computing, and Engineering, Indiana University, IN, USA（卢迪信息学、计算与工程学院，印第安纳大学，印第安纳州，美国）；Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA（放射学与成像科学部，印第安纳大学医学院，印第安纳波利斯，印第安纳州，美国）；Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA（生物统计学与健康数据科学部，印第安纳大学医学院，印第安纳波利斯，印第安纳州，美国）
AI-based Automatic Segmentation of Prostate on Multi-modality Images: A Review
Soochow University（苏州大学）；Peking University First Hospital（北京大学第一医院）；Duke Kunshan University（昆山杜克大学）；Swinburne University of Technology（斯威本科技大学）；The Hong Kong Polytechnic University（香港理工大学）；UT Southwestern Medical Center（德克萨斯大学西南医学中心）
Histopathological Spectrum-Guided Prostate Stratification via Segmentation-Assisted Diagnostic Transformer
College of Artificial Intelligence, Nankai University（南开大学人工智能学院）；Tianjin First Central Hospital（天津市第一中心医院）；School of Electronics and Information Engineering, Tiangong University（天津工业大学电子与信息工程学院）；School of Artificial Intelligence, Hebei University of Technology（河北工业大学人工智能学院）；North China Digital Health Technology Co., Ltd.（华北数字健康科技有限公司）；School of Computing and Information Systems, Singapore Management University（新加坡管理大学计算与信息系统学院）
A Comprehensive Study of Data Augmentation Strategies for Prostate Cancer Detection in Diffusion-weighted MRI using Convolutional Neural Networks
University of Electronic Science and Technology of China（电子科技大学）；University of Toronto（多伦多大学）；The Hospital for Sick Children (SickKids)（SickKids 儿童医院（病童医院））；Sinai Health System（西奈卫生系统）；University Health Network（大学健康网络）；Lunenfeld-Tanenbaum Research Institute（卢嫩菲尔德-塔南鲍姆研究所）；Sunnybrook Research Institute（桑尼布鲁克研究所）
Extracting and analyzing 3D histomorphometric features related to perineural and lymphovascular invasion in prostate cancer
Stanford University（斯坦福大学）；University of Washington（华盛顿大学）；University of Chicago（芝加哥大学）；University of California, Berkeley（加州大学伯克利分校）；Public Health Agency of Catalonia（加泰罗尼亚公共卫生局）；Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）；Atlanta VA Medical Center（亚特兰大退伍军人事务医疗中心）；University of Pennsylvania（宾夕法尼亚大学）
Maximizing T2-Only Prostate Cancer Localization from Expected Diffusion Weighted Imaging
UCL Hawkes Institute and the Department of Medical Physics and Biomedical Engineering, University College London（伦敦大学学院霍克斯研究所与医学物理与生物医学工程系）；Centre for Bioengineering, School of Engineering and Materials Science and Digital Environment Research Institute, Queen Mary University of London（伦敦玛丽女王大学工程与材料科学学院生物工程中心与数字环境研究所）；Centre for Medical Imaging, University College London（伦敦大学学院医学影像中心）；Division of Surgery & Interventional Science, University College London（伦敦大学学院外科与介入科学部）；Urological Research Network（泌尿研究网络）
Prostate Age Gap (PAG): An MRI surrogate marker of aging for prostate cancer detection
University of Stavanger（斯塔万格大学）；Stavanger University Hospital（斯塔万格大学医院）；Karolinska Institutet（卡罗林斯卡学院）；Capio S:t Gorän Hospital（卡皮奥圣戈兰医院）；Danderyd Hospital（丹德里德医院）
Towards Explainable End-to-End Prostate Cancer Relapse Prediction from H&E Images Combining Self-Attention Multiple Instance Learning with a Recurrent Neural Network
University Medical Center Hamburg-Eppendorf（汉堡-埃彭多夫大学医学中心）；Universität Hamburg（汉堡大学）
Interpretable Prostate Cancer Detection using a Small Cohort of MRI Images
Department of Computer Science, Boston University Metropolitan College（波士顿大学计算机科学系）；Department of Radiology, McMaster University（麦斯特大学放射科）；Department of Radiology, School of Medicine, 5th Azar Hospital, Golestan University of Medical Sciences（戈兰大学医学学院放射科，第五阿扎尔医院）；Department of Medical Imaging, Niagara Health System（尼亚加拉健康系统医学影像科）；Center of Excellence in Precision Medicine and Digital Health, Department of Physiology, Chulalongkorn University（朱拉隆梭大学精准医学与数字健康中心，生理学系）
Policy to Assist Iteratively Local Segmentation: Optimising Modality and Location Selection for Prostate Cancer Localisation
Department of Medical Physics and Biomedical Engineering, University College London, London, UK（医学物理与生物医学工程系，伦敦大学学院，伦敦，英国）；UCL Hawkes Institute, University College London, London, UK（UCL Hawkes研究所，伦敦大学学院，伦敦，英国）；School of Computer Science and Engineering, University of Westminster, London, UK（计算机科学与工程学院，伦敦威斯敏斯特大学，伦敦，英国）
Towards Multi-modality Fusion and Prototype-based Feature Refinement for Clinically Significant Prostate Cancer Classification in Transrectal Ultrasound
School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University（深圳大学医学部生物医学工程学院）；Sun Yat-sen University Cancer Center（中山大学肿瘤防治中心）
